For the academic year of 2024/2025, the University of Oxford was ranked as the best university in the world, with an overall score of 98.5 according the Times Higher Education. The Massachusetts Institute of Technology and Harvard University followed behind. A high number of the leading universities in the world are located in the United States, with the ETH Zürich in Switzerland the highest ranked neither in the United Kingdom nor the U.S.

This dataset contains the amount of money paid by UK higher education institutions to ten major publishers (Elsevier, Wiley, Springer, Taylor & Francis, Sage, Oxford University Press, Cambridge University Press, Nature Publishing Group, Royal Society of Chemistry, and Institute of Physics Publishing) for academic journals from 2010-14. The data was obtained by sending FOI requests to each institution through the website whatdotheyknow.com. It now represents over £430m of expenditure. These are ten of the largest academic publishers but do not represent the total spend of these institutions on academic journals. Please see the F1000 data note (http://f1000research.com/articles/3-274/v3) for a full description of the data collection process. For a visualisation of the data go to http://shiny.retr0.me/journal_costs/, and for updated 2015/16 figures go to https://figshare.com/articles/Journal_subscription_expenditure_in_the_UK_2015-16/4542433/3

UPDATE 08/10/2014: Added figures for 13 more institutions. UPDATE 22/10/2014: Added figures for subscriptions to Elsevier journals. Also includes additional figures for other publishers for 16 institutions. UPDATE 24/10/2014: Added figures for subscriptions to Elsevier journals for 13 more institutions. UPDATE 27/10/2014: Added figures for subscriptions to Elsevier journals for 5 more institutions. UPDATE 29/10/2014: Added figures for subscriptions to Elsevier journals for 25 more institutions. UPDATE 5/11/2014: Added figures for subscriptions to Elsevier journals for 14 more institutions. UPDATE 25/11/2014: Added figures for Wiley, Springer, and OUP for 20 Russell Group institutions from Michelle Brook's FOI data (http://dx.doi.org/10.6084/m9.figshare.1250073) as well as further figures for around 20 other institutions. UPDATE 03/12/2014: Added figures for subscriptions to various publishers for 13 institutions. UPDATE 09/12/2014: Added figures for subscriptions to various publishers for 15 institutions. UPDATE 23/12/2014: Added figures for subscriptions to various publishers for 12 institutions. Note on the data 16/01/2015: It has been brought to my attention that the way I have added VAT to some figures may not be 100% accurate. I have added VAT to those figures for which it was not provided (a minority), but since VAT is only applied to electronic and not print publications in the UK, the figures I have added may not be completely accurate. Electronic subscriptions make up the majority of journal expenditure now but not all of it. Please bear this is mind when directly comparing different institutions' figures. I will see revisit the data at some point to see if I can rectify the problem. UPDATE 22/01/2015: Added figures for subscriptions to various publishers for 16 institutions. UPDATE 20/03/2015: Added figures for subscriptions to various publishers for 16 institutions. I have also removed the VAT (17.5% in 2010, 20% in 2011-14) which I had previously added to those figures which excluded VAT. This is to address the issue raised above - in the UK, VAT is only charged on electronic publications and not print. Since I don't know which proportion of the expenditure is on print and which is on electronic, I have decided not to add VAT. This may not make the figures more accurate but at least I can be sure that I am not inflating them. The total is now around £5m lower than before. UPDATE 05/06/2015: Data for three more publishers added (NPG, RSC, and IOP) for 70 institutions. Added figures for various other publishers for three institutions. UPDATES 10/06/2015: Added figures for subscriptions to NPG, RSC, and IOP for 17 institutions. Fixed two formatting errors as noted by D. Himmelstein in the comments. UPDATE 12/06/2015: Added figures for subscriptions to NPG, RSC, and IOP for 20 institutions. UPDATE 17/06/2015: Added figures for subscriptions to NPG, RSC, and IOP for 27 institutions. UPDATE 29/06/2015: Added figures for subscriptions to NPG, RSC, and IOP for 7 institutions. UPDATE 29/07/2015: Added figures for subscriptions to NPG, RSC, and IOP for 5 institutions.

Full resource found at: https://sparcopen.org/our-work/big-deal-knowledge-base

Sourcing: Pricing Data: Individual entries are linked to third party resources within the database; non-linked entries come from Freedom of Information requests (courtesy of Ted Bergstrom and Paul Courant). FTE Data: UK Higher Education Statistics Agency for UK FTE (HE student enrollment FTE + HE staff); DOE IPEDS for US FTE (“Full-time equivalent fall enrollment” + “Total FTE staff”); Universities Canada and COPPUL for Canadian FTE (student data only). Institutional Categories: Carnegie Classification of Institutions of Higher Education.

This database contains the name and roles of board members, supervisory board members, academic advisory boards, and CEO's of all think tank organizations that are part of the Atlas Network/Atlas Economic Research Foundation between January 2021 and December 2022. The dataset covers each continent under separate sections for individual continent analysis. Columns A to D refer to the names of the individuals. Column E refers to the associated think tank organization Column F refers to the position that they hold Column H and F refer to the main employer

There is some missing data where information on main employers was unavailable. All data was collate from each organizations website available to the public at the time of data collection

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2022, is designed for analysts to conduct cross-sectional analysis for the 2022 calendar year. The Calendar Year datasets combine data collected in a specific year from across multiple waves and these are released as separate calendar year studies, with appropriate analysis weights, starting with the 2020 Calendar Year dataset. Each subsequent year, an additional yearly study is released.

The Calendar Year data is designed to enable timely cross-sectional analysis of individuals and households in a calendar year. Such analysis can, however, only involve variables that are collected in every wave (excluding rotating content, which is only collected in some of the waves). Due to overlapping fieldwork, the data files combine data collected in the three waves that make up a calendar year. Analysis cannot be restricted to data collected in one wave during a calendar year, as this subset will not be representative of the population. Further details and guidance on this study can be found in the document 9333_main_survey_calendar_year_user_guide_2022.

These calendar year datasets should be used for cross-sectional analysis only. For those interested in longitudinal analyses using Understanding Society please access the main survey datasets: End User Licence version or Special Licence version.

Understanding Society: the UK Household Longitudinal Study, started in 2009 with a general population sample (GPS) of UK residents living in private households of around 26,000 households and an ethnic minority boost sample (EMBS) of 4,000 households. All members of these responding households and their descendants became part of the core sample who were eligible to be interviewed every year. Anyone who joined these households after this initial wave was also interviewed as long as they lived with these core sample members to provide the household context. At each annual interview, some basic demographic information was collected about every household member, information about the household is collected from one household member, all 16+-year-old household members are eligible for adult interviews, 10-15-year-old household members are eligible for youth interviews, and some information is collected about 0-9 year-olds from their parents or guardians. Since 1991 until 2008/9 a similar survey, the British Household Panel Survey (BHPS), was fielded. The surviving members of this survey sample were incorporated into Understanding Society in 2010. In 2015, an immigrant and ethnic minority boost sample (IEMBS) of around 2,500 households was added. In 2022, a GPS boost sample (GPS2) of around 5,700 households was added. To know more about the sample design, following rules, interview modes, incentives, consent, and questionnaire content, please see the study overview and user guide.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2022 data. One is available under the standard End User Licence (EUL) agreement (SN 9333), and the other is a Special Licence (SL) version (SN 9334). The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see document 9333_eul_vs_sl_variable_differences for more details). Users are advised first to obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2022 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,800 variables.

Abstract copyright UK Data Service and data collection copyright owner.

The computer industry, which includes the production of hardware, software and services, is one of the UK's most important 'high-technology' industries, employing 220,000 workers in 1995. Yet during the 1990s, UK computer firms have been facing intense competitive pressures and rapidly changing technology and market demand. Within this context, this project investigated the impact of changing technology, market demand and globalisation of production upon business organisation, performance and location in this important industry. The research covered both computer hardware manufacturing, which is dominated by foreign direct investment and large companies, and computer software and services, both of which have experienced a considerable growth of smaller indigenous UK companies.
Main Topics:

The dataset contains information regarding the firm's general characteristics; product range; firm's origins and entrepreneurship; subsidiaries; firm development and markets; competition and growth; inter-firm relations; technology and innovation; and investment and finance.

The Evidence for Equality National Survey (EVENS) is a national survey that documents the experiences and attitudes of ethnic and religious minorities in Britain. EVENS was developed by the Centre on the Dynamics of Ethnicity (CoDE) in response to the disproportionate impacts of COVID-19 and is the largest and most comprehensive survey of the lives of ethnic and religious minorities in Britain for more than 25 years. EVENS used pioneering, robust survey methods to collect data in 2021 from 14,200 participants of whom 9,700 identify as from an ethnic or religious minority. The EVENS main dataset, which is available from the UK Data Service under SN 9116, covers a large number of topics including racism and discrimination, education, employment, housing and community, health, ethnic and religious identity, and social and political participation.

The EVENS Teaching Dataset provides a selection of variables in an accessible form to support the use of EVENS in teaching across a range of subjects and levels of study. The dataset includes demographic data and variables to support the analysis of:

• racism and belonging
• health and well-being during COVID-19
• political attitudes and trust.

The dataset contains the background data for recreation or use of the results presented in Figures 2 - 7. The data supports the recent publication that demonstrated how a LangArc model can successfully fit both major and minor hysteresis loops of a bed of magnetic particles in real time using instruments that detect changes in the magnetic field strength, such as in-situ pick-up coils. The data is intelligible when cross-referenced to the respective Figure, its caption and experimental details published in the article. The data presents a full numerical representation of the results published in the article including the direct experimental data and the model fit results for the various cases. The data will support other researchers in developing their own models to fit the results of magnetic hysteresis experiments.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

This release combines fourteen waves of Understanding Society data with harmonised data from all eighteen waves of the BHPS. As multi-topic studies, the purpose of Understanding Society and BHPS is to understand short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move they are followed within the UK and anyone joining their households are also interviewed as long as they are living with them. The study has five sample components: the general population sample; a boost sample of ethnic minority group members; an immigrant and ethnic minority boost sample (from wave 6); participants from the BHPS; and a second general population boost sample added at this wave. In addition, there is the Understanding Society Innovation Panel (which is a separate standalone survey (see SN 6849)). The fieldwork period is for 24 months. Data collection uses computer assisted personal interviewing (CAPI) and web interviews (from wave 7), and includes a telephone mop-up. From March 2020 (the end of wave 10 and the 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended, and the survey was conducted by web and telephone only, but otherwise has continued as before. Face-to-face interviewing was resumed from April 2022. One person completes the household questionnaire. Each person aged 16 is invited to complete the individual adult interview and self-completed questionnaire. Parents are asked questions about their children under 10 years old. Youths aged 10 to 15 are asked to respond to a self-completion questionnaire. For the general and BHPS samples biomarker, genetic and epigenetic data are also available. The biomarker data, and summary genetics and epigenetic scores, are available via UKDS (see SN 7251); detailed genetics and epigenetics data are available by application (see below). In 2020-21 an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). Participants are asked consent to link their data to wide-ranging administrative data sets (see below).

Further information may be found on the Understanding Society Main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence, Special Licence and Secure Access versions:

There are three versions of the main Understanding Society data with different access conditions. One is available under the standard End User Licence (EUL) agreement (this study), one is a Special Licence (SL) version (SN 6931) and the third is a Secure Access version (SN 6676). The SL version contains month as well as year of birth variables, more detailed country and occupation coding for a number of variables, various income variables that have not been top-coded, and other potentially sensitive variables (see 6931_eul_vs_sl_variable_differences document available with the SL version for full details of the differences). The Secure Access version, in addition to containing all the variables in the SL version, also contains day of birth as well as Grid Reference geographical variables. Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL and Secure Access versions of the data have more restrictive access conditions and prospective users of those versions should visit the catalogue entries for SN 6931 and SN 6676 respectively for further information.

Low- and Medium-level geographical identifiers are also available subject to SL access conditions; see SNs 6666, 6668-6675, 7453-4, 7629-30, 7245, 7248-9 and 9169-9170. Schools data are available subject to SL access conditions in SN 7182. Higher Education establishments for Wave 5 are available subject to SL access conditions in SN 8578. Interviewer Characteristics data, also subject to SL access conditions is available in SN 8579. In addition, a fine detail geographic dataset (SN 6676) is available under more restrictive Secure Access conditions that contains National Grid postcode grid references (at 1m resolution) for the unit postcode of each household surveyed, derived from ONS Postcode Directories (ONSPD). For details on how to make an application for Secure Access dataset, please see the SN 6676 catalogue record.

How to access genetic and/or bio-medical sample data from Understanding Society:

Information on how to access genetics and epigenetics data directly from the study team is available on the Understanding Society Accessing data webpage.

Linked administrative data

Linked Understanding Society / administrative data are available on a number of different platforms. See the Understanding Society Data linkage webpage for details of those currently available and how they can be accessed.

Latest edition information

For the 19th edition (November 2024) Wave 14 data has been added. Other minor changes and corrections have also been made to Waves 1-13. Please refer to the revisions document for full details.

m_hhresp and n_hhresp files updated, December 2024

In the previous release (19th edition, November 2024), there was an issue with household income estimates in m_hhresp and n_hhresp where a household resides in a new local authority (approx. 300 households in wave 14). The issue has been corrected and imputation models re-estimated and imputed values updated for the full sample. Imputed values will therefore change compared to the versions in the original release. The variable affected is n_ctband_dv.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain over 2,047 variables.

The data comprises our participant's anonymized scores for the following factors: Trait EI 4 factors (average scores across 7 items for each factor) Trait EI Global score (average scores across 30 items) Neuroticism factor of the Big Five (average scores across 12 NEO-FFI items) Extraversion factor of the Big Five (average scores across 12 NEO-FFI items) Openness factor of the Big Five (average scores across 12 NEO-FFI items) Agreeableness factor of the Big Five (average scores across 12 NEO-FFI items) Conscientiousness factor of the Big Five (average scores across 12 NEO-FFI items) 4 Trait EI sub-IATs scores (D-scores) 5 Big Five sub-IATs scores (D-scores) - The dataset doesn't include any personal or demographic information.

This dataset contains 1789 data instances with problem identification, missing resource, time-dependent questions and answers pairs for disaster management.

There are two data sets of eye scans available. The first of these is a set fundus images of which the are c. 7.0 million. The other is a set of OCT scans of which there are c. 440, 000.

This dataset contains routine clinical ophthalmology data for every patient who have been seen at Queen Elizabeth Hospital and the Birmingham, Solihull and Black Country Diabetic Retinopathy screening program at University Hospitals Birmingham NHS Foundation Trust, with longitudinal follow-up for 15 years. Key data included are: • Total number of patients. • Demographic information (including age, sex and ethnicity) • Past ocular history • Intravitreal injections • Length of time since eye diagnosis • Visual acuity • The national screening diabetic grade category (seven categories from R0M0 to R3M1) • Reason for sight and severe sight impairment

Geography University Hospitals Birmingham is set within the West Midlands and it has a catchment population of circa 5.9million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data source: Ophthalmology department at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic screening scheme in Europe.

Pathway: The routine secondary care follow-up in the hospital eye services for all ophthalmic diseases at Queen Elizabeth Hospital. The Birmingham, Solihull and Black Country dataset is representative of the patient pathway for community screening and grading of diabetic eye disease.

This dataset serves as a benchmark for 3D turbulent channel flows, based on simulations performed using a high-fidelity lattice Boltzmann method (LBM) solver, as described in Xue et al., Phys. Fluids, 34,5, 2022.It comprises 240 trajectories generated from 3D periodic turbulent channel flow simulations with a fixed relaxation time, $\tau = 0.5025$. We extract the central cross-section of the domain along the streamwise ($x$) direction with 3 coordinate components. The spatial resolution is $192 \times 192$, and the friction Reynolds number is set to $Re_{\tau} = 180$, equivalent to $Re = 3250$. The dataset is split into 192 training, 24 validation, and 24 test trajectories, all provided in .npy format.This dataset is designed to facilitate machine learning research in dynamical systems, especially in the challenging context of high-dimensional, turbulent flow regimes.

Data compiled from Post Opening Project Evaluation reports published by Highways England / National Highways for schemes opened 2001-2016. This is the data used in the paper: Chapman, P., (2024) “Project delivery performance: Insights from English roads major schemes” Project Leadership and Society journal https://doi.org/10.1016/j.plas.2024.100128

Abstract copyright UK Data Service and data collection copyright owner.

As the UK went into the first lockdown of the COVID-19 pandemic, the team behind the biggest social survey in the UK, Understanding Society (UKHLS), developed a way to capture these experiences. From April 2020, participants from this Study were asked to take part in the Understanding Society COVID-19 survey, henceforth referred to as the COVID-19 survey or the COVID-19 study.

The COVID-19 survey regularly asked people about their situation and experiences. The resulting data gives a unique insight into the impact of the pandemic on individuals, families, and communities. The COVID-19 Teaching Dataset contains data from the main COVID-19 survey in a simplified form. It covers topics such as

• Socio-demographics
• Whether working at home and home-schooling
• COVID symptoms
• Health and well-being
• Social contact and neighbourhood cohesion
• Volunteering

The resource contains two data files:

• Cross-sectional: contains data collected in Wave 4 in July 2020 (with some additional variables from other waves);
• Longitudinal: Contains mainly data from Waves 1, 4 and 9 with key variables measured at three time points.

Key features of the dataset

• Missing values: in the web survey, participants clicking "Next" but not answering a question were given further options such as "Don't know" and "Prefer not to say". Missing observations like these are recorded using negative values such as -1 for "Don't know". In many instances, users of the data will need to set these values as missing. The User Guide includes Stata and SPSS code for setting negative missing values to system missing.

• The Longitudinal file is a balanced panel and is in wide format. A balanced panel means it only includes participants that took part in every wave. In wide format, each participant has one row of information, and each measurement of the same variable is a different variable.

• Weights: both the cross-sectional and longitudinal files include survey weights that adjust the sample to represent the UK adult population. The cross-sectional weight (betaindin_xw) adjusts for unequal selection probabilities in the sample design and for non-response. The longitudinal weight (ci_betaindin_lw) adjusts for the sample design and also for the fact that not all those invited to participate in the survey, do participate in all waves.

• Both the cross-sectional and longitudinal datasets include the survey design variables (psu and strata).

A full list of variables in both files can be found in the User Guide appendix.

Who is in the sample?

All adults (16 years old and over as of April 2020), in households who had participated in at least one of the last two waves of the main study Understanding Society, were invited to participate in this survey. From the September 2020 (Wave 5) survey onwards, only sample members who had completed at least one partial interview in any of the first four web surveys were invited to participate. From the November 2020 (Wave 6) survey onwards, those who had only completed the initial survey in April 2020 and none since, were no longer invited to participate

The User guide accompanying the data adds to the information here and includes a full variable list with details of measurement levels and links to the relevant questionnaire.

Main Topics:

• Socio-demographics;
• Whether working at home and home-schooling;
• COVID symptoms;
• Health and well-being;
• Social contact and neighbourhood cohesion;
• Volunteering.

College Affordability and Transparency List Explanation Form 2015–16 (CATEF 2015–16) is a cross-sectional data collection that collects information on the major areas of institutions’ budgets with the greatest cost increases, the explanations for these increases, and the steps institutions have been or will be taking towards reducing these costs. The data collection is conducted on the subset of institutions that appear on the tuition and fees and/or net price increase lists for being in the five percent of institutions in their institutional sector that have the highest increases, expressed as a percentage change, over the three-year time period. This data collection is mandatory and expects a 100 percent response rate. Key statistics produced from CATEF 2015–16 are a description of the major areas in the institution's budget with the greatest cost increases; an explanation of the cost increases; a description of the steps the institution will take toward the goal of reducing costs in the areas described; an explanation of the extent to which the institution participates in determining such cost increase; the identification of the agency or instrumentality of state government responsible for determining such cost increase; and any other information the institution considers relevant to the report.

The fMRI data is stored in MATLAB's .mat format, and is designed to be processed in MATLAB software.

PitVQA dataset comprises 25 videos of endoscopic pituitary surgeries from the National Hospital of Neurology and Neurosurgery in London, United Kingdom, similar to the dataset used in the MICCAI PitVis challenge. All patients provided informed consent, and the study was registered with the local governance committee. The surgeries were recorded using a high-definition endoscope (Karl Storz Endoscopy) with a resolution of 720p and stored as MP4 files. All videos were annotated for the surgical phases, steps, instruments present and operation notes guided by a standardised annotation framework, which was derived from a preceding international consensus study on pituitary surgery workflow. Annotation was performed collaboratively by 2 neurosurgical residents with operative pituitary experience and checked by an attending neurosurgeon. We extracted image frames from each video at 1 fps and removed any frames that were blurred or occluded. Ultimately, we obtained a total of 109,173 frames, with the videos of minimum and maximum length yielding 2,443 and 7,179 frames, respectively. We acquired frame-wise question-answer pairs for all the categories of the annotation. Overall, there are 884,242 question-answer pairs from 109,173 frames, which is around 8 pairs for each frame. There are 59 classes overall, including 4 phases, 15 steps, 18 instruments, 3 variations of instruments present in a frame, 5 positions of the instruments, and 14 operation notes in the annotation classes. The length of the questions ranges from a minimum of 7 words to a maximum of 12 words.The details description of the original videos can be found at the MICCAI PitVis challenge and the videos can be directly download from UCL HDR portal.

Anxiety and depression are the most prevalent classes of mental illnesses; rates of anxiety and depression have been exacerbated due to the COVID-19 pandemic. Vulnerability to anxiety and depression are affected by risk and resilience factors, such as personality constructs. Recent research (e.g., Lyon et al, 2020; 2021) suggests that, out of all 30 NEO-PI-R personality constructs, variance in anxiety and depression are explained by a small number of personality constructs. However it is unclear which mechanisms mediate the relationship between these personality constructs and anxiety and depression. The purpose of this study was to investigate the mediating effect of emotion regulation strategies on the relationship between personality constructs and COVID-related anxiety and depression. Data were collected from a sample of 210 students at the University of Manchester. Measures included a select number of narrow Big Five personality facets which explain variance in anxiety and depression (facets depression, assertiveness, gregariousness, positive emotion and competence), select COPE Inventory strategies associated with coping with pandemics, and COVID-related anxiety and depression. Measures of COPE strategies and mental health were adapted to refer to coping and mental health in response to COVID pandemic.

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2022: Special Licence Access, is designed for analysts to conduct cross-sectional analysis for the 2022 calendar year. The Calendar Year datasets combine data collected in a specific year from across multiple waves and these are released as separate calendar year studies, with appropriate analysis weights, starting with the 2020 Calendar Year dataset. Each subsequent year, an additional yearly study is released.

The Calendar Year data is designed to enable timely cross-sectional analysis of individuals and households in a calendar year. Such analysis can however, only involve variables that are collected in every wave (excluding rotating content which is only collected in some of the waves). Due to overlapping fieldwork the data files combine data collected in the three waves that make up a calendar year. Analysis cannot be restricted to data collected in one wave during a calendar year, as this subset will not be representative of the population. Further details and guidance on this study can be found in the document 9334_main_survey_calendar_year_user_guide_2022.

These calendar year datasets should be used for cross-sectional analysis only. For those interested in longitudinal analyses using Understanding Society please access the main survey datasets: End User Licence version or Special Licence version.

Understanding Society: the UK Household Longitudinal Study, started in 2009 with a general population sample (GPS) of UK residents living in private households of around 26,000 households and an ethnic minority boost sample (EMBS) of 4,000 households. All members of these responding households and their descendants became part of the core sample who were eligible to be interviewed every year. Anyone who joined these households after this initial wave, were also interviewed as long as they lived with these core sample members to provide the household context. At each annual interview, some basic demographic information was collected about every household member, information about the household is collected from one household member, all 16+ year old household members are eligible for adult interviews, 10-15 year old household members are eligible for youth interviews, and some information is collected about 0-9 year olds from their parents or guardians. Since 1991 until 2008/9 a similar survey, the British Household Panel Survey (BHPS), was fielded. The surviving members of this survey sample were incorporated into Understanding Society in 2010. In 2015, an immigrant and ethnic minority boost sample (IEMBS) of around 2,500 households was added. In 2022 a GPS boost sample (GPS2) of around 5,700 households was added. To know more about the sample design, following rules, interview modes, incentives, consent, questionnaire content please see the study overview and user guide.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2022 data. One is available under the standard End User Licence (EUL) agreement (SN 9333), and the other is a Special Licence (SL) version (SN 9334). The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see 9334_eul_vs_sl_variable_differences for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2022 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,800 variables.